Keratin-A6ACA NPs for gastric ulcer diagnosis and repair

Ding, Yi; Meng, Run; Yin, Haimeng; Hou, Zongkun; Sun, Changfa; Liu, Wenjie; Hao, Shilei; Pan, Yun; Wang, Bochu

doi:10.1007/s10856-021-06537-3

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…These events lead to the liberation of pro-inf lammatory cytokines, including TNF. [9,10] The factors outlined are crucial contributors to the escalated inflammatory reaction triggered by ethanol, the resultant oxidative stress-induced damage, tissue necrosis, apoptosis of gastric mucosal cells, and direct harm to the mucosal layer. [11] Although there exists a wide array of medications for treating gastric ulcers, including proton pump inhibitors, anti-histamines, and antacids, many of these drugs come with adverse side effects like gynecomastia and arrhythmias.…”

Section: Abstract a R T I C L E I N F Omentioning

confidence: 99%

PROTECTIVE ACTION OF ROOTS OF <i>ABROMA AUGUSTA</i> IN GASTRIC ULCER PREVENTION INDUCED BY ETHANOL IN RATS

Das,

Cyriac

2024

Int. J. Pharm. Sci. Drug Res.

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Ulcers result from imbalanced aggressive factors (acid, pepsin, NSAIDs) and defensive factors (mucosal barrier). Free radical-associated damage, is managed by antioxidant enzymes like Superoxide dismutases and Catalase. The objective of the study is to investigate the protective action of roots of Abroma augusta in gastric ulcer prevention induced by ethanol in rats. 30 animals were divided into 5 groups (n=6). Group I and II were administered normal saline (2ml/kg/p.o), Group III was administered Omeprazole 20 mg/kg, and Group IV and V were administered root extract (250mg/kg/p.o and 500mg/kg/p.o) for 21 days. After that, the animals were fasted for 24 hours before the ethanol administration (1ml/200gm) except Group I. Parameters like Ulcer Index, % inhibition of ulceration, plasma anti-oxidant enzyme levels, and Histopathology were assessed. The present study revealed that the root extract at 500mg/kg possesses significant antiulcer and antioxidant properties, which may be the presence of tannins, saponins, alkaloids, and terpenoids. The extract effectively countered ethanol-induced lesion formation and maintained plasma catalase, superoxide dismutase, and lipid peroxidase levels. The root extract at 500mg/kg was able to significantly protect the histological structure of the gastric mucosa and prevent the infiltration of inflammatory cells. This study has provided documentary evidence for the antiulcer property of Abroma augusta for its activity.

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Section: Abstract a R T I C L E I N F Omentioning

confidence: 99%

PROTECTIVE ACTION OF ROOTS OF <i>ABROMA AUGUSTA</i> IN GASTRIC ULCER PREVENTION INDUCED BY ETHANOL IN RATS

Das,

Cyriac

2024

Int. J. Pharm. Sci. Drug Res.

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“…Training using analog circuits for diagnostic models in fault diagnosis is more valuable. With the continuous refinement of various data-analysis methods, the troubleshooting technology for analog circuits is also improving; common troubleshooting techniques, including PCA [1], Search Grid [2], particle swarm optimization (PSO) [3], ant colony algorithm (ACA) [4], simulated annealing (SA) [5], genetic algorithm (GA) [6], Back Propagation Neural Network (BP) [6], Self-organizing Maps (SOM) [7], Extreme Learning Machine (ELM) [8], decision tree [9], random forest [10] and SVM [11] all have good classification results to some extent [12].…”

Section: Introductionmentioning

confidence: 99%

TLSCA-SVM Fault Diagnosis Optimization Method Based on Transfer Learning

Zhang

2022

Processes

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In fault-diagnosis classification, a pressing issue is the lack of target-fault samples. Obtaining fault data requires a great amount of time, energy and financial resources. These factors affect the accuracy of diagnosis. To address this problem, a novel fault-diagnosis-classification optimization method, namely TLSCA-SVM, which combines the sine cosine algorithm and support vector machine (SCA-SVM) with transfer learning, is proposed here. Considering the availability of fault data, this thesis uses the data generated by analog circuits from different faults for analysis. Firstly, the data signal is collected from different faults of the analog circuit, and then the characteristic data are extracted from the data signals by the wavelet packets. Secondly, to employ the principal component analysis (PCA) reduces the feature-value dimension. Lastly, as an auxiliary condition, the error-penalty item is added to the objective function of the SCA-SVM classifier to construct an innovative fault-diagnosis model namely TLSCA-SVM. Among them, the Sallen–Key bandpass filter circuit and the CSTV filter circuit are used to provide the data for horizontal- and vertical-contrast classification results. Comparing the SCA with the five optimization algorithms, it is concluded that the performance of SCA optimization parameters has certain advantages in the classification accuracy and speed. Additionally, to prove the superiority of the SCA-SVM classification algorithm, the five classification algorithms are compared with the SCA-SVM algorithm. Simulation results showed that the SCA-SVM classification has higher precision and a faster response time compared to the others. After adding the error penalty term to SCA-SVM, TLSCA-SVM requires fewer fault samples to process fault diagnosis. Ultimately, the method which is proposed could not only perform fault diagnosis effectively and quickly, but also could run effectively to achieve the effect of transfer learning in the case of less failure data.

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